13,724 research outputs found
Cosmic Swarms: A search for Supermassive Black Holes in the LISA data stream with a Hybrid Evolutionary Algorithm
We describe a hybrid evolutionary algorithm that can simultaneously search
for multiple supermassive black hole binary (SMBHB) inspirals in LISA data. The
algorithm mixes evolutionary computation, Metropolis-Hastings methods and
Nested Sampling. The inspiral of SMBHBs presents an interesting problem for
gravitational wave data analysis since, due to the LISA response function, the
sources have a bi-modal sky solution. We show here that it is possible not only
to detect multiple SMBHBs in the data stream, but also to investigate
simultaneously all the various modes of the global solution. In all cases, the
algorithm returns parameter determinations within (as estimated from
the Fisher Matrix) of the true answer, for both the actual and antipodal sky
solutions.Comment: submitted to Classical & Quantum Gravity. 19 pages, 4 figure
Detecting compact galactic binaries using a hybrid swarm-based algorithm
Compact binaries in our galaxy are expected to be one of the main sources of
gravitational waves for the future eLISA mission. During the mission lifetime,
many thousands of galactic binaries should be individually resolved. However,
the identification of the sources, and the extraction of the signal parameters
in a noisy environment are real challenges for data analysis. So far,
stochastic searches have proven to be the most successful for this problem. In
this work we present the first application of a swarm-based algorithm combining
Particle Swarm Optimization and Differential Evolution. These algorithms have
been shown to converge faster to global solutions on complicated likelihood
surfaces than other stochastic methods. We first demonstrate the effectiveness
of the algorithm for the case of a single binary in a 1 mHz search bandwidth.
This interesting problem gave the algorithm plenty of opportunity to fail, as
it can be easier to find a strong noise peak rather than the signal itself.
After a successful detection of a fictitious low-frequency source, as well as
the verification binary RXJ0806.3+1527, we then applied the algorithm to the
detection of multiple binaries, over different search bandwidths, in the cases
of low and mild source confusion. In all cases, we show that we can
successfully identify the sources, and recover the true parameters within a
99\% credible interval.Comment: 19 pages, 5 figure
Gravitational waves: search results, data analysis and parameter estimation
The Amaldi 10 Parallel Session C2 on gravitational wave (GW) search results, data analysis and parameter estimation included three lively sessions of lectures by 13 presenters, and 34 posters. The talks and posters covered a huge range of material, including results and analysis techniques for ground-based GW detectors, targeting anticipated signals from different astrophysical sources: compact binary inspiral, merger and ringdown; GW bursts from intermediate mass binary black hole mergers, cosmic string cusps, core-collapse supernovae, and other unmodeled sources; continuous waves from spinning neutron stars; and a stochastic GW background. There was considerable emphasis on Bayesian techniques for estimating the parameters of coalescing compact binary systems from the gravitational waveforms extracted from the data from the advanced detector network. This included methods to distinguish deviations of the signals from what is expected in the context of General Relativity
A Bayesian Approach to the Detection Problem in Gravitational Wave Astronomy
The analysis of data from gravitational wave detectors can be divided into
three phases: search, characterization, and evaluation. The evaluation of the
detection - determining whether a candidate event is astrophysical in origin or
some artifact created by instrument noise - is a crucial step in the analysis.
The on-going analyses of data from ground based detectors employ a frequentist
approach to the detection problem. A detection statistic is chosen, for which
background levels and detection efficiencies are estimated from Monte Carlo
studies. This approach frames the detection problem in terms of an infinite
collection of trials, with the actual measurement corresponding to some
realization of this hypothetical set. Here we explore an alternative, Bayesian
approach to the detection problem, that considers prior information and the
actual data in hand. Our particular focus is on the computational techniques
used to implement the Bayesian analysis. We find that the Parallel Tempered
Markov Chain Monte Carlo (PTMCMC) algorithm is able to address all three phases
of the anaylsis in a coherent framework. The signals are found by locating the
posterior modes, the model parameters are characterized by mapping out the
joint posterior distribution, and finally, the model evidence is computed by
thermodynamic integration. As a demonstration, we consider the detection
problem of selecting between models describing the data as instrument noise, or
instrument noise plus the signal from a single compact galactic binary. The
evidence ratios, or Bayes factors, computed by the PTMCMC algorithm are found
to be in close agreement with those computed using a Reversible Jump Markov
Chain Monte Carlo algorithm.Comment: 19 pages, 12 figures, revised to address referee's comment
Powellsnakes II: a fast Bayesian approach to discrete object detection in multi-frequency astronomical data sets
Powellsnakes is a Bayesian algorithm for detecting compact objects embedded
in a diffuse background, and was selected and successfully employed by the
Planck consortium in the production of its first public deliverable: the Early
Release Compact Source Catalogue (ERCSC). We present the critical foundations
and main directions of further development of PwS, which extend it in terms of
formal correctness and the optimal use of all the available information in a
consistent unified framework, where no distinction is made between point
sources (unresolved objects), SZ clusters, single or multi-channel detection.
An emphasis is placed on the necessity of a multi-frequency, multi-model
detection algorithm in order to achieve optimality
An analysis of I/O efficient order-statistic-based techniques for noise power estimation in the HRMS sky survey's operational system
Noise power estimation in the High-Resolution Microwave Survey (HRMS) sky survey element is considered as an example of a constant false alarm rate (CFAR) signal detection problem. Order-statistic-based noise power estimators for CFAR detection are considered in terms of required estimator accuracy and estimator dynamic range. By limiting the dynamic range of the value to be estimated, the performance of an order-statistic estimator can be achieved by simpler techniques requiring only a single pass of the data. Simple threshold-and-count techniques are examined, and it is shown how several parallel threshold-and-count estimation devices can be used to expand the dynamic range to meet HRMS system requirements with minimal hardware complexity. An input/output (I/O) efficient limited-precision order-statistic estimator with wide but limited dynamic range is also examined
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